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Suggested Citation:"Contents." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25974.
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Suggested Citation:"Contents." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25974.
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Suggested Citation:"Contents." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25974.
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Suggested Citation:"Contents." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25974.
×
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Suggested Citation:"Contents." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25974.
×
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Suggested Citation:"Contents." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25974.
×
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Suggested Citation:"Contents." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25974.
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v Contents Summary 1 1 Introduction and Background 4 1.1 Introduction 4 1.2 Transportation Planning Programs and Cost Forecasting 6 1.3 Current Practice vs. Ideal Practice 10 1.4 Cost Indexes and Inflation Rates 12 1.4.1 Matching and Proportionality Principles 13 1.4.2 Types of Inflation Rates 14 1.5 Organization of the Report 15 2 State-of-the-Practice of Cost Forecasting 17 2.1 Introduction 17 2.2 Transportation Planning Programs and Cost Forecasting Periods 18 2.2.1 Configuration of Program Cost Estimates 19 2.3 Forecasting Tools and Methods 20 2.4 Current Use of Construction Cost Indexes and Inflation Rates 22 2.5 Risk Analysis Practices in Cost Forecasting 24 3 Case Study Methodology 26 3.1 Introduction 26 3.2 Case Study Objective 26 3.3 Case Study Methodology 26 3.4 Historical Bid Data Collection and Cleaning 27 3.4.1 Outlier Detection and Removal 28 3.5 Existing Cost Indexing Alternatives 29 3.6 Multilevel Construction Cost Index 30 3.6.1 Defining Basket of Pay Items for MCCI 31 3.6.2 MCCI Configuration and Calculation 31 3.6.3 Development of Scope-Based CCIs 36 3.6.4 MCCI Versions Under Consideration 40 3.6.5 Regional Levels of Competition in MCCIs 44

vi 4.1 Introduction 46 4.2 Purpose of Chapter 46 4.3 Overview of Comparative Suitability Analysis Protocol for Cost Indexes 47 4.3.1 Representative Pay Items and Analysis Period 47 4.3.2 Bid Data Point Clouds 48 4.3.3 Base Power Regression Curves and Base Unit Price Estimates 49 4.3.4 Index-Based Data Point Clouds 49 4.3.5 Average Distance Between Bid Data and Index-Based Data Point Clouds and Identification of the Most Suitable Cost Indexing Alternative 49 4.4 Chapter Findings 50 5. Cost Forecasting Approaches 52 5.1 Introduction 52 5.2 Standard Annual Inflation Rates 52 5.3 Risk-Based Forecasting Timelines from MFE Results 56 5.4 Linear and Exponential Regression Analysis 59 5.5 Chapter Findings 61 6. Implementing Formal Cost Forecasting Practices 63 6.1 Introduction 63 6.2 Expert Feedback and Input 63 6.3 Cost Forecasting Approach Selection Framework 64 6.4 Cost Forecasting Toolkit 65 6.5 NCHRP Implementation Support Program 66 References 67 Abbreviations and Acronyms 71 Appendix A - Historical Bid Data Summary 72 Appendix B – Configuration of Multilevel Construction Cost Indexes 78 4. Identification of Suitable Construction Cost Index 46

vii List of Figures Figure 1.1 Cost Forecasting Uncertainty over Time. 9 Figure 1.2 Current Typical Cost Forecasting Process. 10 Figure 1.3 Ideal Cost Forecasting Process. 11 Figure 1.4 Fixed Compounded Inflation Rate vs. Fixed Simple Inflation Rate 15 Figure 2.1 Survey Responses and Policy Documents Reviewed. 17 Figure 2.2 Forecasting Time Horizons and Updating Frequency of Transportation Programs. 18 Figure 2.3 Cost Estimate Configuration per Transportation Program – Survey Responses (Survey Responses = 18). 20 Figure 2.4 Cost Forecasting Methods and Tools (Survey Responses = 17). 20 Figure 2.5 STA Staff with Economics and/or Statistics Background (Survey Responses = 17).21 Figure 2.6 Information Technology Tools used in Cost Forecasting (Survey Responses = 17). 22 Figure 2.7 Compounded Interest Rate vs. Simple Interest Rate (Survey Responses = 16). 24 Figure 3.1 Case Study Methodology 27 Figure 3.2 Example of Tidy Dataset – MnDOT’s Tidy Dataset 28 Figure 3.3 Example of MCCI Configuration – CDOT MCCI 32 Figure 3.4 MnDOT Unit Price Model for Common Excavation 2008-2012. 34 Figure 3.5 Example of Project-Specific CCI – MnDOT’s Asphalt Paving Project 38 Figure 3.6 Example of Program-Specific CCI – MnDOT Paving Program 39 Figure 3.7 MnDOT Geographic Regions 42 Figure 3.8 CDOT Geographic Regions 43 Figure 3.9 DelDOT Geographic Regions 43 Figure 3.10 Number of Contract Awarded by MnDOT per Period (2009-2018) 45 Figure 4.1 Cons Indexing Comparative Suitability Analysis Protocol 47 Figure 5.1 Example of MFE Output – Average Forecasting Errors for DelDOT’s Asphalt Paving Activities with a 4% Compounded Annual Inflation Rate 54 Figure 5.2 Example of MFE Output – Average Forecasting Errors with Confidence Intervals for DelDOT’s Asphalt Paving Activities with a 4% Compounded Annual Inflation Rate 55 Figure 5.3 Example of Risk-Based Forecasting Timeline with 4% Compounded Projection 57 Figure 5.4 Example of Risk-Based Forecasting Timeline with 3.1% Compounded Projection 58 Figure 5.5 MnDOT Asphalt Paving CCI – North Region – Linear Regression Model 60 Figure 5.6 MnDOT Asphalt Paving CCI – North Region – Exponential Regression Model 60 Figure 6.1 Overall Cost Forecasting Approach Selection Framework 64

viii List of Tables Table 3.1 Summary of Collected Historical Bid Data per Agency 27 Table 3.2 Existing Construction Cost Indexes 30 Table 3.3 CDOT’s MCCI Levels and Configuration 35 Table 3.4 Number of Cost Indexes per Level per Case Study Agency 36 Table 3.5 Asphalt Paving Project – MnDOT Sample Project 37 Table 3.6 Example of Project-Specific CCI – MnDOT’s Sample Asphalt Paving Project 38 Table 3.7 MnDOT - Multilevel Construction Cost Index Classification 40 Table 3.8 CDOT - Multilevel Construction Cost Index Classification 41 Table 3.9 DelDOT - Multilevel Construction Cost Index Classification 41 Table 4.1 Selected Relevant Items for Comparison Analysis 48 Table 4.2 Case Study Results - Top Three Cost Indexing Alternatives per Region 50 Table 5.1 Consolidation of Standard Inflation Rates from Case Studies 55 Table 5.2 Consolidated Forecasting Error Ranges from the Application of the MFE Method 59 Table 6.1 Description of the Cost Forecasting Approach Selection Framework Modules 65 This document is the Final Report for NCHRP Project 10-101 and supplements NCHRP Research Report 953: Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidance for State Transportation Agencies. Readers can read or purchase NCHRP Research Report 953 at www.TRB.org.

1 Summary In a transportation planning context, cost estimating is an iterative process intended to predict the amount of monetary resources that will be required to undertake anticipated infrastructure investments. The iterative part of this process refers to the fact that those estimates are subjected to multiple revisions as they move across a series of planning phases. Since all revisions still pertain to future investments, they are all based on assumptions of future construction market conditions. The part of the cost estimating process intended to directly or indirectly account for those assumptions is called cost forecasting. Initial cost forecasting efforts are usually performed under Long-Range Transportation Plans (LRTPs), which as per federal regulations, are required to cover a period of no less than 20 years. Program cost forecasts at this early stage are based on broad infrastructure performance goals and calculated under several assumptions with minimum or no project-specific information. The first budget revision for some agencies occurs when planned construction activities move from the LRTP into an intermediate-range plan (IRP), which is a 10- to 15-year program. Those activities are eventually moved into the State Transportation Improvement Program (STIP) as they get within 4 to 5 years of their anticipated letting dates, where budgets are further refined in the light of better-defined scopes of work at the project level. Predicting the future of the construction market is always a challenging task, regardless of whether it is over the next 1 or 20 years since it involves several uncertainties. The study presented in this report is intended to assist state transportation agencies (STAs) to overcome those challenges by developing a Cost Forecasting Approach Selection Framework designed to facilitate the selection and implementation of effective mid-term (3 to 5 years), intermediate-range (up to 15 years), and long-range (more than 15 years) cost forecasting procedures. The study was conducted under the National Cooperative Highway Research Program (NCHRP) Project 10-101, “Improving Mid- Term, Intermediate, and Long-Range Cost Forecasting: Guidance for State Departments of Transportation.” The report presents a summary of the research efforts, discusses the main project findings, and includes a general description of traditional and novel cost forecasting approaches evaluated by the research team. The Cost Forecasting Approach Selection Framework and some technical information on the proposed forecasting approaches (e.g., mathematical and statistical procedures) are explained in detail in NCHRP Research Report 953: Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidance for State Transportation Agencies. The study used both qualitative and quantitative methods to assess the effectiveness of various cost forecasting practices along different forecasting time horizons. This assessment was conducted through case studies with three STAs: the state departments of transportation in Minnesota, Colorado, and Delaware. Significant data collection, cleaning, and processing efforts were required to evaluate existing cost forecasting practices, as well as to develop and validate novel practices proposed by the research team to fill identified gaps in the forecasting process. Twenty years of pricing data from each agency were processed in this study.

2 The study found two essential elements involved in the cost forecasting process: construction cost indexes (CCIs) and annual inflation rates. In general terms, a CCI is a time series aimed to quantify average price fluctuations in the construction market over time while an annual inflation rate is a simplified representation of the expected future behavior of the construction market and is usually determined through the analysis of a CCI. Thus, the effectiveness of the cost forecasting process relies on two critical factors: 1) the selection of a suitable CCI and 2) the appropriate analysis of the selected index to produce reliable inflation rates. The ability to identify the most suitable CCI would be useless if the agency does not have the analytical skills to generate reliable inflation rates out of the CCI. Likewise, the ability to produce effective inflation rates out of a CCI would not be sufficient if the composition of the selected CCI does not fairly align with the scope of work under consideration. With regard to the first identified critical factor, the study found that current cost forecasting practices tend to underestimate the importance of selecting a cost index that matches the intended scope of work by assuming that a single CCI can be applied to all types of work. This study challenged that assumption by demonstrating that forecasting effectiveness significantly improves with the use of scope-based CCIs. Scope-based CCIs were generated using an innovative cost indexing system called a Multilevel Construction Cost Index (MCCI), which is described in Chapter 3 of this report. Recognizing that not all STAs would invest resources in the development and implementation of an MCCI, the proposed guidelines also consider the use of traditional CCIs. Guidelines were also formulated for STAs that prefer the use of standard industry inflation rates without an in-house assessment of market trends (no CCIs used). To address the second critical factor, the research team proceeded to evaluate the effectiveness of various approaches to generate inflation rates from CCIs, including the use of simple and compounded inflation rates, as well as regression analysis and an alternative method proposed by the research team called Moving Forecasting Error (MFE). Those approaches were evaluated on their forecasting accuracy and reliability over different forecasting time horizons; their ability to factor geographic considerations and program-/project-specific requirements; and their associated staffing, data, and technical requirements. All project findings were consolidated into the Cost Forecasting Approach Selection Framework and a spreadsheet-based Cost Forecasting Toolkit, also described in the Transportation Cost Forecasting Guidebook. The Cost Forecasting Approach Selection Framework is divided into five modules.  Module 1. Cost Index Selection: This module guides STAs on the selection of suitable cost index taking into consideration staffing, resource, and information technology constraints.  Module 2. Standard Inflation Rate Selection: If an STA decides not to perform an in-house analysis on a cost index to determine an annual inflation rate, this module provides suggestions on annual inflation rates based on results from the case studies.  Module 3. Mid-Term Forecasting Method Selection: This model assists with the selection of cost forecasting approaches for mid-term forecasts (3 to 5 years).

3  Module 4. Intermediate-Range Forecasting Method Selection: This model assists with the selection of cost forecasting approaches for intermediate-range forecasts (up to 15 years).  Module 5. Long-Range Forecasting Method Selection: This model assists with the selection of cost forecasting approaches for long-range forecasts (more than 15 years). A recorded presentation and slides that summarize this research are available on the TRB website at http://www.trb.org/main/blurbs/181094.aspx.

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Predicting the future of the construction market is always a challenging task - regardless of whether it is over the next one or 20 years - since it involves several uncertainties.

The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 283: Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies documents the research that led to the development of a Cost Forecasting Approach Selection Framework that can assist state transportation agencies to select and implement effective mid-term (3 to 5 years), intermediate-range (up to 15 years), and long-range (more than 15 years) cost forecasting procedures.

Supplemental information to the technical report includes NCHRP Research Report 953: Improving Mid-Term, Intermediate,and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies, a presentation, and videos.

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